Download and run Arm-optimized object detection models from the Arm AI Portal on an Arm-powered Android phone or emulator using ExecuTorch, LiteRT, and XNNPACK.
Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time object detection from live video and image files using DetectNet and TensorRT.
Evaluation Conditions
Object Detection · Raspberry Pi 5 · INT8 · 640×640 · COCO 2017 (val2017)
| Metric | Resultsvs baseline | Change | |
|---|---|---|---|
| End to end latency p50 | 258.07ms618.28ms | −360.22 ms | |
| End to end latency p90 | 258.76ms618.75ms | −359.99 ms | |
| End to end latency p99 | 259.83ms620.80ms | −360.97 ms | |
| Peak memory | 109.44MB154.58MB | −45.14 MB | |
| Average memory | 108.55MB153.94MB | −45.39 MB | |
| Time to first inference | 276.76ms643.82ms | −367.06 ms | |
| Frames per second | 3.87fps1.62fps | +2.25 fps | |
| mAP@0.50:0.95 | 36.09%37.10% | −1.01% | |
| mAP@0.50 | 54.97%55.60% | −0.63% | |
| mAP@0.75 | 39.71%40.46% | −0.75% |
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| Configuration | Baseline | Arm Optimized |
|---|---|---|
| Dataset | COCO 2017 (val2017) | COCO 2017 (val2017) |
| Sample count | 5,000 | 5,000 |
| Batch size | 1 | 1 |
| Input resolution | 640x640 | 640x640 |
| Runs | 100 | 100 |
| Warmup runs | 10 | 10 |